def Loves(x,y):
if not ran:
if x == X or x == Y or y == X or y == Y:
print "Error: variables not allowed in declarative phase."
return False
global bradb
# inject stimulus object
injectBrain(bradb, agent, rates[x], 0.0, parameters['LEARNING'])
injectBrain(bradb, object, rates[y], 0.0, parameters['LEARNING'])
print "Stored"
else:
lX=loadBrain(parameters['BRAINNAME']+'-'+x+'Report.txt')
lA=loadBrain(parameters['BRAINNAME']+'-'+agent+'Report.txt')
lY=loadBrain(parameters['BRAINNAME']+'-'+y+'Report.txt')
lO=loadBrain(parameters['BRAINNAME']+'-'+object+'Report.txt')
_,spikesX=brainalyze.countSpikes(lX,10000*parameters['LEARNING'])
_,spikesA=brainalyze.countSpikes(lA,10000*parameters['LEARNING'])
_,spikesY=brainalyze.countSpikes(lY,10000*parameters['LEARNING'])
_,spikesO=brainalyze.countSpikes(lO,10000*parameters['LEARNING'])
xAg = brainalyze.corr(spikesX,spikesA)
yAg = brainalyze.corr(spikesY,spikesA)
xObj = brainalyze.corr(spikesX,spikesO)
yObj = brainalyze.corr(spikesY,spikesO)
if x == mary:
xAgc=xAg/ma_nl
yAgc=yAg/ja_nl
if x == john:
xAgc=xAg/ja_nl
yAgc=yAg/ma_nl
if y == mary:
yObjc=yObj/mo_nl
xObjc=xObj/jo_nl
if y == john:
yObjc=yObj/jo_nl
xObjc=xObj/mo_nl
print "X Agent, Y Agent ", xAg,yAg
print "X Object, Y Object ", xObj,yObj
# print "Coeff xAg/yAg ", xAg/yAg
# print "Coeff yObj/xObj", yObj/xObj
print "(Corrected) X Agent, Y Agent ", xAgc,yAgc
print "(Corrected) X Object, Y Object ", xObjc,yObjc
if xAgc > yAgc and yObjc > xObjc:
return True
else:
return False
def qLoves(x,y):
global parameters
lB=bsim.loadBrain('vb-exp-BReport.txt')
lG=bsim.loadBrain('vb-exp-GReport.txt')
lT=bsim.loadBrain('vb-exp-TReport.txt')
lS=bsim.loadBrain('vb-exp-SReport.txt')
_,spikesB=brainalyze.countSpikes(lB,10000*(parameters['ENDSIM']/2))
_,spikesG=brainalyze.countSpikes(lG,10000*(parameters['ENDSIM']/2))
_,spikesT=brainalyze.countSpikes(lT,10000*(parameters['ENDSIM']/2))
_,spikesS=brainalyze.countSpikes(lS,10000*(parameters['ENDSIM']/2))
if x[1]=='B':
spikesA=spikesB
elif x[1]=='G':
spikesA=spikesG
if y[1]=='B':
spikesO=spikesB
elif y[1]=='G':
spikesO=spikesG
cBT=brainalyze.corr(spikesB,spikesT)#/(brainalyze.corr(spikesT,spikesS)/1)
cGT=brainalyze.corr(spikesG,spikesT)#/(brainalyze.corr(spikesT,spikesS)/1)
cBS=brainalyze.corr(spikesB,spikesS)#/(brainalyze.corr(spikesS,spikesT)/1)
cGS=brainalyze.corr(spikesG,spikesS)#/(brainalyze.corr(spikesS,spikesT)/1)
cAT=brainalyze.corr(spikesA,spikesT)#/(brainalyze.corr(spikesS,spikesT)/1)
cOT=brainalyze.corr(spikesO,spikesT)#/(brainalyze.corr(spikesS,spikesT)/1)
cAS=brainalyze.corr(spikesA,spikesS)#/(brainalyze.corr(spikesS,spikesT)/1)
cOS=brainalyze.corr(spikesO,spikesS)#/(brainalyze.corr(spikesS,spikesT)/1)
print "john = Lover (cBT)", cBT*100
print "mary = Lover (cGT)", cGT*100
print "john = Lovee (cBS)", cBS*100
print "mary = Lovee (cGS)", cGS*100
print "x = Lover (cAT)", cAT*100
print "y = Lover (cOT)", cOT*100
print "x = Lovee (cAS)", cAS*100
print "y = Lovee (cOS)", cOS*100
if x != y and cAT > cOT and cOS > cAS:
return True
elif x == y:
# this is a bit of a hack, but can be made not to depend on x or y
if x==john and cBT > cBS and cGT > cGS and cBT >= cGT and cBS >= cGS:
return True
elif x==mary and cBS > cBT and cGS > cGT and cGT >= cBT and cGS >= cBS:
return True
else:
return False
else:
return False
def run_analysis():
x=0
y=0
corrBT=np.zeros((max_x,max_y)) # Blue Triangle
corrGT=np.zeros((max_x,max_y)) # Green Triangle
corrBS=np.zeros((max_x,max_y)) # Blue Square
corrGS=np.zeros((max_x,max_y)) # Green Square
frateB=np.zeros((max_x,max_y)) # Blue firing rate
frateG=np.zeros((max_x,max_y)) # Green firing rate
frateT=np.zeros((max_x,max_y)) # Triangle firing rate
frateS=np.zeros((max_x,max_y)) # Square firing rate
while x < max_x:
while y < max_y:
print x,y
spikesB, spikesG, spikesT, spikesS = brainalyze.loadExperimentBind4('vb-exp', x, y)
corrBT[x][y]=brainalyze.corr(spikesB,spikesT)
corrGT[x][y]=brainalyze.corr(spikesG,spikesT)
corrBS[x][y]=brainalyze.corr(spikesB,spikesS)
corrGS[x][y]=brainalyze.corr(spikesG,spikesS)
frateB[x][y]=brainalyze.frate(spikesB)
frateG[x][y]=brainalyze.frate(spikesG)
frateT[x][y]=brainalyze.frate(spikesT)
frateS[x][y]=brainalyze.frate(spikesS)
y=y+1
y=0 #reset
x=x+1
# Save all numeric data
np.save('corrBT.npy',corrBT)
np.save('corrGT.npy',corrGT)
np.save('corrBS.npy',corrBS)
np.save('corrGS.npy',corrGS)
np.save('frateB.npy',frateB)
np.save('frateG.npy',frateG)
np.save('frateT.npy',frateT)
np.save('frateS.npy',frateS)
return (corrBT,corrGT,corrBS,corrGS,frateB,frateG,frateT,frateS)
lX=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-XReport.txt')
lY=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-YReport.txt')
lAP=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-APReport.txt')
lOP=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-OPReport.txt')
lAQ=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-AQReport.txt')
lOQ=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-OQReport.txt')
lI=brainsim8.loadBrain('vb-exp'+str(x)+'-'+str(y)+'-IReport.txt')
_,spikesJ=brainalyze.countSpikes(lJ,10000*(parameters['ENDSIM']/2))
_,spikesM=brainalyze.countSpikes(lM,10000*(parameters['ENDSIM']/2))
_,spikesX=brainalyze.countSpikes(lX,10000*(parameters['ENDSIM']/2))
_,spikesY=brainalyze.countSpikes(lY,10000*(parameters['ENDSIM']/2))
_,spikesAP=brainalyze.countSpikes(lAP,10000*(parameters['ENDSIM']/2))
_,spikesOP=brainalyze.countSpikes(lOP,10000*(parameters['ENDSIM']/2))
_,spikesAQ=brainalyze.countSpikes(lAQ,10000*(parameters['ENDSIM']/2))
_,spikesOQ=brainalyze.countSpikes(lOQ,10000*(parameters['ENDSIM']/2))
cJX=brainalyze.corr(spikesJ,spikesX)
cMX=brainalyze.corr(spikesM,spikesX)
cJY=brainalyze.corr(spikesJ,spikesY)
cMY=brainalyze.corr(spikesM,spikesY)
cXAP=brainalyze.corr(spikesX,spikesAP)
cXOP=brainalyze.corr(spikesX,spikesOP)
cXAQ=brainalyze.corr(spikesX,spikesAQ)
cXOQ=brainalyze.corr(spikesX,spikesOQ)
cYAP=brainalyze.corr(spikesY,spikesAP)
cYOP=brainalyze.corr(spikesY,spikesOP)
cYAQ=brainalyze.corr(spikesY,spikesAQ)
cYOQ=brainalyze.corr(spikesY,spikesOQ)
avg = (abs(cJX - cJY) + abs(cMY - cMX) + abs(cXAP - cXAQ) + abs(cXOQ - cXOP) + abs(cXAQ - cXAP) + abs(cXOP - cXOQ))/6
plt.subplot(514)
plt.plot(sp,lS,'b')
plt.subplot(515)
plt.plot(sp,lI,'b')
plt.show()
brainsim.simBrain(parameters)
lB=brainsim.loadBrain('vb-exp-BReport.txt')
lG=brainsim.loadBrain('vb-exp-GReport.txt')
lT=brainsim.loadBrain('vb-exp-TReport.txt')
lS=brainsim.loadBrain('vb-exp-SReport.txt')
_,spikesB=brainalyze.countSpikes(lB,10000*(parameters['ENDSIM']/2))
_,spikesG=brainalyze.countSpikes(lG,10000*(parameters['ENDSIM']/2))
_,spikesT=brainalyze.countSpikes(lT,10000*(parameters['ENDSIM']/2))
_,spikesS=brainalyze.countSpikes(lS,10000*(parameters['ENDSIM']/2))
cBT=brainalyze.corr(spikesB,spikesT)/(brainalyze.corr(spikesT,spikesS)/1)
cGT=brainalyze.corr(spikesG,spikesT)/(brainalyze.corr(spikesT,spikesS)/1)
cBS=brainalyze.corr(spikesB,spikesS)/(brainalyze.corr(spikesS,spikesT)/1)
cGS=brainalyze.corr(spikesG,spikesS)/(brainalyze.corr(spikesS,spikesT)/1)
#cBS=cBS+10
#cGS=cGS+10
#cBS=(cBS/cGS)*cBT
#cGS=cBT
print "Blue Triangle", cBT*100
print "Green Triangle", cGT*100
print "Blue Square", cBS*100
print "Green Square", cGS*100
#plotUse()